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Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition

CVPR 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 442 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add multi-scale patch fusion rather than use a single patch size (ablation)Patch-NetVLAD with RANSAC scoring · Mapillary Val. setR@1: 77.8 -> 79.5Helped
Replace RANSAC with rapid spatial scoring while retaining one patch size (ablation)Patch-NetVLAD · Mapillary Val. setR@1: 77.8 -> 77.2Hurt
Replace RANSAC with rapid spatial scoring while retaining multiple patch sizes (ablation)Patch-NetVLAD · Mapillary Val. setR@1: 79.5 -> 78.2Hurt
Add multi-scale fusion while using rapid spatial scoring (ablation)Patch-NetVLAD · Mapillary Val. setR@1: 77.2 -> 78.2Helped
Use rapid spatial verification instead of RANSAC (ablation)Patch-NetVLAD with one patch size · Place recognition datasetsprocessing speed: 2.9 times fasterHelped
Use rapid spatial verification instead of RANSAC (ablation)Patch-NetVLAD with multiple patch sizes · Place recognition datasetsprocessing speed: 3.1 times fasterHelped
Use rapid spatial scoring instead of RANSAC (ablation)Patch-NetVLAD with one patch size · Place recognition datasetsrecall: 0.6%Hurt
Use rapid spatial scoring instead of RANSAC (ablation)Patch-NetVLAD with multiple patch sizes · Place recognition datasetsrecall: 1.1%Hurt
Increase patch descriptor PCA dimension (ablation)Patch-NetVLAD · Mapillary validation setR@1Mixed
Vary the single patch size away from the intermediate-size peak (ablation)Single-Patch-NetVLAD · Mapillary validation setrecallHurt

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